SRGAN based super-resolution reconstruction of power inspection images
Abstract Ensuring the operational safety of the electric power system critically depends on effective power inspections. However, traditional methods face challenges in detecting minor faults such as cracks and corrosion in electrical equipment. In this thesis, the Super Resolution Generative Advers...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-024-06350-x |
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| _version_ | 1846147547516633088 |
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| author | Jianjun Zhou Jianbo Zhang Jiangang Jia Jie Liu |
| author_facet | Jianjun Zhou Jianbo Zhang Jiangang Jia Jie Liu |
| author_sort | Jianjun Zhou |
| collection | DOAJ |
| description | Abstract Ensuring the operational safety of the electric power system critically depends on effective power inspections. However, traditional methods face challenges in detecting minor faults such as cracks and corrosion in electrical equipment. In this thesis, the Super Resolution Generative Adversarial Network (SRGAN) is introduced into the field of power inspection for the first time. Additionally, the dedicated dataset (BDZ dataset) was developed. This includes a large number of high-resolution images for power line inspection. The primary objective is to enhance the resolution of inspection images, thereby significantly improving the accuracy and reliability of defect detection in the power system. Numerous experiments have demonstrated that the SRGAN model outperforms traditional models in the super-resolution reconstruction of power inspection images, particularly in recovering image texture details. Using the BDZ dataset significantly enhances image resolution. When employing the same SRGAN model, PSNR increased by 2.47 dB and SSIM by 4.10% compared to the standard dataset. This research introduces new methodologies for advancing electric power inspection technologies, providing a more robust assurance for the safe and reliable operation of electric power systems. |
| format | Article |
| id | doaj-art-e7f6f45f341d496889f038007f07a1d0 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-e7f6f45f341d496889f038007f07a1d02024-12-01T12:39:56ZengSpringerDiscover Applied Sciences3004-92612024-11-0161211610.1007/s42452-024-06350-xSRGAN based super-resolution reconstruction of power inspection imagesJianjun Zhou0Jianbo Zhang1Jiangang Jia2Jie Liu3State Grid Henan Electric Power Company Xichuan County Power Supply CompanyState Grid Henan Electric Power Company Xichuan County Power Supply CompanyState Grid Henan Electric Power Company Xichuan County Power Supply CompanyCollege of Electrical and Information Engineering, Zhengzhou University of Light IndustryAbstract Ensuring the operational safety of the electric power system critically depends on effective power inspections. However, traditional methods face challenges in detecting minor faults such as cracks and corrosion in electrical equipment. In this thesis, the Super Resolution Generative Adversarial Network (SRGAN) is introduced into the field of power inspection for the first time. Additionally, the dedicated dataset (BDZ dataset) was developed. This includes a large number of high-resolution images for power line inspection. The primary objective is to enhance the resolution of inspection images, thereby significantly improving the accuracy and reliability of defect detection in the power system. Numerous experiments have demonstrated that the SRGAN model outperforms traditional models in the super-resolution reconstruction of power inspection images, particularly in recovering image texture details. Using the BDZ dataset significantly enhances image resolution. When employing the same SRGAN model, PSNR increased by 2.47 dB and SSIM by 4.10% compared to the standard dataset. This research introduces new methodologies for advancing electric power inspection technologies, providing a more robust assurance for the safe and reliable operation of electric power systems.https://doi.org/10.1007/s42452-024-06350-xSuper-resolution reconstructionPower inspectionsGenerative adversarial networksBDZ dataset |
| spellingShingle | Jianjun Zhou Jianbo Zhang Jiangang Jia Jie Liu SRGAN based super-resolution reconstruction of power inspection images Discover Applied Sciences Super-resolution reconstruction Power inspections Generative adversarial networks BDZ dataset |
| title | SRGAN based super-resolution reconstruction of power inspection images |
| title_full | SRGAN based super-resolution reconstruction of power inspection images |
| title_fullStr | SRGAN based super-resolution reconstruction of power inspection images |
| title_full_unstemmed | SRGAN based super-resolution reconstruction of power inspection images |
| title_short | SRGAN based super-resolution reconstruction of power inspection images |
| title_sort | srgan based super resolution reconstruction of power inspection images |
| topic | Super-resolution reconstruction Power inspections Generative adversarial networks BDZ dataset |
| url | https://doi.org/10.1007/s42452-024-06350-x |
| work_keys_str_mv | AT jianjunzhou srganbasedsuperresolutionreconstructionofpowerinspectionimages AT jianbozhang srganbasedsuperresolutionreconstructionofpowerinspectionimages AT jiangangjia srganbasedsuperresolutionreconstructionofpowerinspectionimages AT jieliu srganbasedsuperresolutionreconstructionofpowerinspectionimages |